From blurry numbers to clear preferences: A mechanism to extract reputation in social networks

Complex social networks are typically used in order to represent and structure social relationships that do not follow a predictable pattern of behaviour. Due to their openness and dynamics, these networks make participants continuously deal with uncertainty before any type of interaction. Reputation appears as a key concept helping users to mitigate such uncertainty. Most of the reputation mechanisms proposed in the literature are based on numerical opinions (ratings), and consequently, they are exposed to potential problems such as the subjectivity in the opinions and their consequent inaccurate aggregation. With these problems in mind, this paper presents a reputation mechanism based on the concepts of pairwise elicitation processes and knock-out tournaments. The main objective of this mechanism is to build reputation rankings from qualitative opinions, thereby removing the subjectivity problems associated with the aggregation of quantitative opinions. The proposed approach is evaluated with different data sets from the MovieLens and Flixster web sites.

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